Mattia Bruschetta
Papers
3
Total Citations
8
H-Index
2
About
Mattia Bruschetta is a researcher whose work sits at the intersection of advanced control systems, autonomous navigation, and human-vehicle interaction. His primary contributions lie in developing real-time estimation and trajectory generation algorithms for complex dynamic systems, with a particular focus on automotive and aerospace applications. Bruschetta’s most cited paper (2021, 4 citations) introduces a velocity-aided, correlated noise Extended Kalman Filter (CEKF) for attitude estimation, demonstrated on a motorcycle case study—a critical advancement for vehicle guidance and robotics. He further extends his expertise to space robotics with a 2025 paper (2 citations) that employs nonlinear model predictive control (NMPC) for online trajectory generation of satellite-mounted manipulators, enabling adaptive, real-time responses to unforeseen scenarios. His 2019 work (2 citations) bridges autonomous and human driving performance using the DiM driving simulator, combining objective metrics with subjective characterization to assess assisted driving systems. Bruschetta’s research is notable for its practical impact on real-world control challenges, from two-wheeled vehicles to orbital robotics, showcasing a versatile skill set in estimation theory, optimal control, and human factors engineering.
Research Focus
Key Achievements
Top Papers
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